Search Results (39)

This study provides a comprehensive review on monocular metrology and fuzzy random-set portfolio management of financial assets. The findings and conclusions are elaborated as follows. Soft computing and AI have already enhanced and will further empower a variety of applications of monocular metrology and fuzzy random-set portfolio management of financial assets through progressive quantification and capturing of domain situations. The single most significant limitation of monocular metrology lies in its intrinsic incapability of direct measurement of 3D geometry through 2D imagery. The future of monocular metrology lies in deep learning and end-to-end solutions, multi-sensor data fusion, algorithmic optimization and real-time performance, self-supervised learning and generalization, and standardization and practical deployment. Neither statistical validation nor performance optimization alone is sufficient to support decision-makers in making portfolio decisions that are reliably and trust-worthy. A promising portfolio management decision-making framework should integrate the statistical rigor of fuzzy statistics with fuzzy random portfolio optimization techniques to quantitatively account for fuzziness and uncertainty while better balancing computational efficiency, statistical reliability, interpretability, and practical credibility. Full article

Multi-criteria decision-making (MCDM) involves evaluating alternatives under uncertain, vague, and conflicting criteria. While fuzzy set theories, such as intuitionistic, pythagorean, fermatean, and q-rung orthopair fuzzy sets have advanced uncertainty modeling, they remain limited to capturing extreme judgments where membership reaches a value of one alongside significant non-membership. The recently introduced q-fractional fuzzy set (q-FrFS) addresses these shortcomings via a flexible constraint, making it suitable for extreme contexts. However, existing q-FrFS methodologies lack robust aggregation mechanisms capable of balancing trade-offs and modulating compensation during information fusion. To overcome this, this study proposes a novel class of Frank-based aggregation operators tailored specifically to q-FrFS environments. Leveraging the parameterized structure of Frank t-norms and t-conorms, we develop two operators: q-FrFFWA (Frank weighted averaging) and q-FrFFWG (Frank weighted geometric) alongside their essential algebraic properties. These operators enhance the representation and fusion of complex and uncertain data. Furthermore, we present a comprehensive MCDM framework utilizing the proposed operators and demonstrate its applicability by selecting optimal vehicle routing software for last-mile delivery. Sensitivity and comparative analyses affirm the stability and credibility of the proposed methodology. This research contributes to the evolving landscape of fuzzy decision-making by integrating the expressive power of q-FrFS with the adaptive flexibility of Frank aggregation, offering a potent tool for modeling and analyzing multidimensional uncertainties in complex decision environments. Full article

Multi-criteria decision-making (MCDM) problems in complex evaluation systems are often characterized by high uncertainty in expert judgments and dynamic variations in indicator importance. Traditional analytic hierarchy process (AHP) and entropy-based weighting methods typically suffer from two inherent limitations: the inability to explicitly quantify expert hesitation and the rigidity of static weight assignment under evolving data distributions. To address these challenges, this paper proposes a dynamic hybrid weighting framework that integrates an interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) with an entropy-triggered correction mechanism. First, interval-valued intuitionistic fuzzy numbers are employed to simultaneously model membership, non-membership, and hesitation degrees in pairwise comparisons, enabling a more comprehensive representation of expert uncertainty. Second, an entropy-triggered dynamic fusion strategy is developed by jointly incorporating information entropy and coefficient of variation, allowing adaptive adjustment between subjective expert weights and objective data-driven weights. This mechanism effectively enhances sensitivity to high-dispersion criteria while preserving expert knowledge in low-variability indicators. The proposed framework is formulated in a hierarchical fuzzy decision structure and implemented through a fuzzy comprehensive evaluation process. Its feasibility and robustness are validated through a concrete case study on teaching effectiveness evaluation for a university engineering course, leveraging multi-source data. Comparative analysis demonstrates that the proposed approach effectively mitigates the weight rigidity and evaluation inflation observed in conventional methods. Furthermore, it improves diagnostic resolution and decision stability across different evaluation periods. The results indicate that the proposed entropy-triggered IVIF-AHP framework provides a mathematically sound and practically applicable solution for dynamic MCDM problems under uncertainty, with strong potential for extension to other complex evaluation and decision-support systems. Full article

To address the heavy reliance on experiential knowledge, fragmented multi-source information, and limited intelligence in decision-making for aircraft skin spraying processes, this paper proposes a knowledge reasoning method based on a knowledge graph. The authors construct a knowledge graph that integrates multi-structure ontology and physical rule constraints. This graph systematically organizes and manages multi-dimensional knowledge, including painting object attributes, paint performance indicators, and spraying parameters. On this basis, a three-stage reasoning mechanism with multi-granularity semantic understanding, knowledge enhancement, feature fusion, and multi-constraint intelligent matching (MKM) is designed. The model can perform semantic analysis of the user’s fuzzy query, implicit knowledge completion, and dynamic subgraph matching, so as to give the aircraft skin spraying process plan that meets the constraints of safety, compatibility, and feasibility. The experimental results show that the proposed method is superior to the traditional case-based reasoning method, graph convolutional network method, and knowledge graph embedding method in the key evaluation indices of Hit@1, Hit@3, and MRR in the knowledge reasoning task of aircraft skin spraying process. It also has good robustness and promotion value when data are scarce and parameters are uncertain. This study provides a feasible method of intelligent management and dynamic decision-making in terms of aircraft skin spraying process knowledge, and may be applied to other manufacturing fields. Full article

The selection of a final operating point from a Pareto front set of marine diesel engine configurations relies on the critical task of translating operator priorities into quantitative criterion weights. This study isolates this pivotal weighting step and introduces an operator-defined fuzzy weighting module that maps linguistic importance ratings to normalized weights. This module systematically maps important ratings for Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM) into a set of normalized weights for the Multi-Criteria Decision-Making method. The module’s core is a Mamdani-type fuzzy logic module that utilizes triangular membership functions and centroid defuzzification. These fuzzy weights are integrated with the TriMetric Fusion algorithm to generate a robust consensus ranking. Validation on a Pareto front from a two-stroke diesel engine demonstrates the framework’s efficacy: a Fuel-Economy priority selected a configuration with SFC advantage, while a Strict Environmental Compliance priority correctly identified dual emissions strengths. Furthermore, the system effectively mediated trade-offs in a high-competition scenario. Rank correlation analysis confirmed that while the Pareto front nature of the alternatives leads to inherent similarities in rankings, the fuzzy weights induce significant and logical divergences. Future work will focus on validation with real operator feedback and comparative studies with traditional weighting methods. Full article

Wind power generation forecasting is a key technology for wind power projects. It directly determines the stability of grid integration and the accuracy of power dispatching. The interval belief rule base (IBRB) is an uncertainty modeling method; it can be applied to wind power generation forecasting. On the one hand, IBRB uses fixed interval matching. This method tends to cause boundary jumps when predicting continuously variable parameters, which threatens the stability of the grid integration. On the other hand, IBRB underutilizes the correlation information of adjacent intervals in modeling, and its rule activation mechanism limits expressions of complex generation mechanisms. To address these issues, a method based on belief rule base with fuzzy reference value (BRB-f) for wind power generation forecasting is proposed. Firstly, the method replaces fixed interval matching with fuzzy membership functions to reduce the impact of wind power output fluctuations on the grid. Then, through a multi-rule-weighted fusion mechanism and optimization algorithms, it improves the accuracy of scheduling under complex generation mechanisms. Finally, the effectiveness and accuracy of the model are validated using a wind turbine power generation forecasting dataset. It provides a better method choice to ensure grid integration safety and enhance the scientific basis of power dispatch decisions. Full article

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

The Extra-Long Weir Construction method for deep foundation pit construction is crucial for urban underground development. However, as excavation projects become deeper and more complex, construction safety risks increase markedly. Existing monitoring technologies and numerical simulation models face persistent challenges: high uncertainty in risk occurrence, complex environmental interactions, and difficulties in extracting effective warning signals from multi-source data. To address these challenges, this study establishes a systematic risk evaluation framework comprising 6 primary and 29 secondary indicators through Fault Tree Analysis and develops a novel DL-MSD (Deep Learning and Multi-Source Data Prediction) model integrating CNN, ResUnit, and LSTM networks for spatiotemporal sequence analysis and multi-source data fusion. Validated using 6524 samples from the Jinji Lake Tunnel project, the model employs single-factor prediction for hazard source tracing and multi-factor fusion for comprehensive risk assessment. Results demonstrate exceptional performance: 90.2% average accuracy for single-factor warnings and 77.1% for multi-factor fusion, with, critically, all severe warnings (Level I risks) identified with zero omissions. Comparative analysis with T-S fuzzy neural networks, EWT-NARX, and Random Forest confirmed superior accuracy and computational efficiency. An integrated platform incorporating BIM and IoT technologies enables automated monitoring, intelligent prediction, and adaptive control. This study establishes a data-driven intelligent early warning framework that significantly improves prediction accuracy, timeliness, and reliability in deep foundation pit construction, marking a paradigm shift from reactive response to proactive prevention. The findings provide theoretical and methodological support for safety management in ultra-deep excavation projects, offering reliable decision-making evidence for enhancing construction safety and risk management. Full article

Meteorological observations are essential for climate modelling, prediction, early warning systems, decision-making processes, and disaster management. These observations are critical to societal development and the safeguarding of human activities and livelihoods. Spatial interpolation techniques play a pivotal role in addressing gaps between observation sites, enabling the generation of continuous meteorological datasets. However, due to the inherent complexity of atmosphere–surface interactions, no single interpolation technique has proven universally effective in achieving consistently accurate results for meteorological variables. This study proposes a novel interpolation model based on Fuzzy Adaptive Optimal Fusion (FAOF). The FAOF model integrates fuzzy theory by constructing station-specific fuzzy sets and sub-method element pools, employing a nonlinear membership function with error as the independent variable. An iterative accuracy index is used to identify the optimal parameter combination, facilitating adaptive data fusion and interpolation optimisation. The model’s performance is evaluated against 10 individual methods from the method pool. Experimental results demonstrate that FAOF effectively combines the strengths of multiple methods, achieving significantly enhanced interpolation accuracy. Additionally, the model consistently performs well across diverse regions and meteorological variables, underscoring its robustness and strong generalisation capability. Full article

In response to the threat assessment challenge posed by unmanned aerial vehicles (UAVs) in air defense operations, this paper proposes a dynamic assessment model grounded in fuzzy multi-attribute decision making. First, a three-dimensional evaluation index system is established, encompassing capability, opportunity, and intention. Quantification functions for assessing the threat level of each attribute are then designed. To account for the temporal dynamics of the battlefield, an innovative fusion approach is developed, integrating inverse Poisson distribution time weights with subjective–objective comprehensive weighting, thereby establishing a dynamic variable weight fusion mechanism. Among these, the subjective weights are determined by integrating the intention probability matrix, effectively incorporating the intentions into the threat assessment process to reflect their dynamic changes and enhancing the overall evaluation accuracy. Leveraging the improved technique for order preference by similarity to ideal solution (TOPSIS), the model achieves threat prioritization. Experimental results demonstrate that this method significantly enhances the reliability of threat assessments in uncertain and dynamic battlefield environments, offering valuable support for air defense command and control systems. Full article

Background/Objectives: Lung cancer is a leading cause of cancer-related mortalities, with early diagnosis crucial for survival. While biopsy is the gold standard, manual histopathological analysis is time-consuming. This research enhances lung cancer diagnosis through deep learning-based feature extraction, fusion, optimization, and classification for improved accuracy and efficiency. Methods: The study begins with image preprocessing using an adaptive fuzzy filter, followed by segmentation with a modified simple linear iterative clustering (SLIC) algorithm. The segmented images are input into deep learning architectures, specifically ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152), for feature extraction. The extracted features are fused using a deep-weighted averaging-based feature fusion (DWAFF) technique, producing ResNet-X (RN-X)-fused features. To further refine these features, particle swarm optimization (PSO) and red deer optimization (RDO) techniques are employed within the selective feature pooling layer. The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). A performance evaluation is performed using K-fold cross-validation with K values of 2, 4, 5, 8, and 10. Results: The proposed DWAFF technique, combined with feature selection using RDO and classification with MLP, achieved the highest classification accuracy of 98.68% when using K = 10 for cross-validation. The RN-X features demonstrated superior performance compared to individual ResNet variants, and the integration of segmentation and optimization significantly enhanced classification accuracy. Conclusions: The proposed methodology automates lung cancer classification using deep learning, feature fusion, optimization, and advanced classification techniques. Segmentation and feature selection enhance performance, improving diagnostic accuracy. Future work may explore further optimizations and hybrid models. Full article

Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection (ANDFRF). The suggested ANDFRF primarily consists of two steps: in the first step, five machine learning algorithms, comprising K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGbooost (XGB) and Light Gradient Boosting Machine (LightGBM), were utilized as base classifiers for the initial identification of Android Apps either as goodware or malware apps. Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. Furthermore, the rankings reflect the relative importance or acceptance of each class across multiple classifiers, providing deeper insights into the ensemble’s decision-making process. The proposed framework was validated on two publicly accessible datasets, CICAndMal2020 and DREBIN, with a 5-fold cross-validation technique. The proposed ensemble framework achieves a classification accuracy of 95.51% and an AUC of 95.40% on the DREBIN dataset. On the CICAndMal2020 LBC dataset, it attains an accuracy of 95.31% and an AUC of 95.30%. Experimental results demonstrate that the proposed scheme is both efficient and effective for Android malware detection. Full article

In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions. Full article

As an extension of quantitative temporal logic, uncertain temporal logic essentially describes the temporal behavior of uncertain and incomplete systems, thus better solving search and decision-making problems in such systems. Fuzzy linear temporal logic (FLTL) is a focal point in uncertain temporal logic research. However, there are evident shortcomings in the current research outcomes. First, in previous FLTL studies, the practice of obtaining path reachability and formula satisfaction values independently and subsequently selecting the smaller of the two as the satisfaction value metric led to information loss. Furthermore, this simplistic information fusion approach fails to reflect the varying importance of these two types of information to the requirements. Second, computing path reachability and temporal logic formula satisfaction values separately may result in a mismatch between the two pieces of information with respect to the same path segment. Thus, the primary challenge lies in accurately integrating the satisfaction values of temporal logic formulas with the path reachability of the segments that yields these satisfaction values, utilizing various reasonable information synthesis methods, to ensure synchronization between path reachability and formula satisfaction values without incurring information loss. Additionally, it is crucial to reflect the different preference requirements for these two types of information. Moreover, the temporal logic formula characterizes system properties, with its sub-formulas delineating distinct sub-properties. Consequently, considering the varying importance preferences of sub-formulas is also significant. To address these deficiencies, we introduced quality constraint operators into FLTL, resulting in quality-constrained fuzzy linear temporal logic (QFLTL). This incorporation enables the synchronization and comprehensive fusion of path-reachability information and formula satisfaction values within the final semantic metric, thereby resolving the issues related to information synchronization and loss. Furthermore, it can accommodate the differing preference requirements between the two types of information and sub-properties during the information synthesis process. We defined the syntax and semantics of QFLTL and examined its expressive power and properties. Notably, we investigated the decidability of logical decision problems in QFLTL, encompassing validity, satisfiability, and model-checking issues. We proposed corresponding solution algorithms and analyzed their complexities. Full article

The product design process, fraught with uncertainties and ambiguities in its requirements and constraints, commonly traverses multiple stages, each emphasizing distinct design aspects. This engenders heterogeneity in decision-making criteria, rendering the effective integration of information from various stages of product design decision-making (PDDM) a pivotal task in identifying the optimal design solution. Surprisingly, limited research has attended to the challenge of consolidating such heterogeneous information across multiple PDDM stages. To bridge this gap, our study employs real numbers, interval numbers, and linguistic terms to capture the heterogeneous judgments of decision-makers. We fuse the Maximization Deviation Method with the analytic hierarchy process (AHP) for determining indicators’ weights, while decision-makers’ weights are derived through a dual consideration of uncertainty measure using fuzzy entropy and a distance-minimization model applied to the PDDM matrix for achieving consistency. Leveraging the advantage of axiomatic design, product design alternatives are evaluated based on their PDDM information content of PDDM matrices. Given the multistage nature of product design, stages’ weights are computed by assessing the information content and consistency degree of PDDM matrices at each stage. Ultimately, our approach achieves multistage heterogeneous decision-making fusion in product design through information axiom weighting. A case study involving the decision-making process for a specific numerical control machine design illustrates the efficacy of our method in integrating multistage heterogeneous PDDM data, yielding a comprehensive perspective on the viability of product design schemes. Results show that the ranking sequence of the product design schemes solidifies to x₃ > x₂ > x₁ in stages 2 and 3 of PDDM, diverging from the initial order observed in stage 1 (x₂ > x₃ > x₁), while the fused result from the multistage heterogeneous PDDM analysis aligns with the later stages’ rankings, indicating the credibility and persuasiveness are fortified. This methodology thus offers a robust framework for synthesizing and navigating the uncertainties and complexities inherent in multistage heterogeneous PDDM contexts. Full article